Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health

Introduction: Endotracheal intubation (ETI) in the prehospital setting poses unique challengeswhere multiple ETI attempts are associated with adverse patient outcomes. Early identificationof difficult ETI cases will allow providers to tailor airway-man agement efforts to minimizecomplications associated with ETI. We sought to derive and validate a prehospital difficult airwayidentification tool based on predictors of difficult ETI in other settings.

Methods: We prospectively collected patient and airway data on all airway attempts from 16Advanced Life Support (ALS) ground emergency medical services (EMS) agencies from January2011 to October 2014. Cases that required more than two ETI attempts and cases where analternative airway strategy (e.g. supraglottic airway) was employed after one unsuccessful ETIattempt were categorized as “difficult.” We used a random allocation sequence to split the datainto derivation and validation subsets. Using backward elimination, factors with a p<0.1 wereincluded in the multivariable regression for the derivation cohort and then tested in the validationcohort. We used this model to determine the area under the curve (AUC), and the sensitivity andspecificity for each cut point in both the derivation and valida tion cohorts.

Results: We collected data on 1,102 cases with 568 in the derivation set (155 difficult cases;27%) and 534 in the validation set (135 difficult cases; 25%). O f the collected variables,five factors were predictive of difficult ETI in the derivation m odel (adjusted odds ratio, 95%confidence interval [CI]): Glasgow coma score [GCS] >3 (2.15, 1. 19-3.88), limited neckmovement (2.24, 1.28-3.93), trismus/jaw clenched (2.24, 1.09-4. 6), inability to palpate thelandmarks of the neck (5.92, 2.77-12.66), and fluid in the airwa y such as blood or emesis (2.25,1.51-3.36). This was the most parsimonious model and exhibited good fit (Hos mer-Lemeshowtest p = 0.167) with an AUC of 0.68 (95% CI [0.64-0.73]). When applied to the validatio n set,the model had an AUC of 0.63 (0.58-0.68) with high specificity for identifying di fficult ETI if >2factors were present (87.7% (95% CI [84.1-90.8])).

Conclusion: We have developed a simple tool using five factors that may aid p rehospitalproviders in the identification of difficult ETI.